OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

The surgical management of empyema (excluding those caused by mycobacterium tuberculosis and non-tuberculous mycobacteria) is rapidly evolving towards minimally invasive, precise, and stepwise approaches. The traditional three-stage classification (exudative, fibrinopurulent, and organizing) has limitations in guiding dynamic clinical decision-making. For the first time, this consensus explicitly identifies two critical junctures in the pathological progression of empyema: "early transformation" (stage Ⅰ to Ⅱ) and "late transformation" (stage Ⅱ to Ⅲ), and thereby constructs a corresponding "identification-early warning-intervention" stepwise therapeutic framework. The consensus emphasizes that proactive debridement via video-assisted thoracoscopic surgery should be performed during the early transformation phase to halt disease progression. Conversely, during the late transformation phase, therapeutic goals should be rationally adjusted to prioritize adequate drainage, avoiding futile pleural decortication. Moreover, the consensus underscores the pivotal role of precise perioperative etiological diagnosis (e.g. metagenomic nest-generation sequencing) and standardized anti-infective therapy. Integrating practical experiences from multiple thoracic surgery centers in China and relevant evidence-based literature, this consensus formulates recommendations on the precise definitions of staging, surgical indications for each phase, key technical points, perioperative management, and training systems. It aims to promote the standardized and individualized surgical management of empyema, ultimately optimizing patient prognosis.

Osteonecrosis of femoral head （ONFH） is a disease that causes osteocyte death due to interruption of blood supply of the femoral head， which may cause symptoms such as joint movement disorders， pain， and even femoral head collapse. Currently， the commonly used methods for treating ONFH in clinical practice are accompanied by problems such as long-term side effects， inaccurate efficacy， technical limitations， and complication risks. Traditional Chinese medicine （TCM） has shown unique advantages of holistic regulation， mild side effects， and symptom alleviation. A review of the related literature shows that Salviae Miltiorrhizae Radix et Rhizoma， as a representative Chinese herbal medicine for activating blood and resolving stasis， has the effects of activating blood， resolving stasis， dredging meridians， and relieving pain. Its active ingredients （such as salvianolic acid B， tanshinone Ⅱ_A， and tanshinone Ⅰ） interfere with bone metabolism balance， osteogenesis and differentiation， and improve bone microcirculation and bone microstructure. Salviae Miltiorrhizae Radix et Rhizoma is often combined with the herbal medicines with the effects of tonifying the liver and spleen or activing blood to form herb pairs （such as Salviae Miltiorrhizae Radix et Rhizoma-Angelicae Sinensis Radix， Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma， and Salviae Miltiorrhizae Radix et Rhizoma-Drynariae Rhizoma）， which enhances the efficacy through synergistic effects. In the application of TCM compound formulas， Bushen Huoxue prescription， Bushen Huoxue capsules， Gubi Tongxiao granules， and Shenggu Zaizao pills can regulate signaling pathways such as Wnt/β-catenin， Hedgehog， Notch， transforming growth factor（TGF）-β₁/Smads， and mitogen-activated protein kinase （MAPK） through multiple targets to significantly improve bone metabolism and hip joint function， playing an important role in preventing and treating ONFH. Through a review of available studies， this paper systematically explains the outstanding advantages of the active ingredients， herb pairs， and compound formulas of Salviae miltiorrhizae Radix et Rhizoma in the prevention and treatment of ONFH. The mechanisms may be related to factors such as promoting osteoblast generation and inhibiting osteoclast formation， increasing osteogenic differentiation， promoting angiogenesis， increasing bone density， inhibiting inflammatory response， and regulating signaling pathways. In the future， modern technology can be employed to optimize dosage forms， and high-quality clinical research should be conducted to further clarify its mechanism of action and promote the precise application of TCM. This review aims to provide more thinking directions for the application of Salviae miltiorrhizae Radix et Rhizoma in preventing and treating ONFH.

Biliary atresia （BA） is characterized by progressive inflammation and fibrous obstruction of bile ducts， ultimately leading to cholestatic liver cirrhosis. Kasai surgery is the standard procedure for the treatment of BA， and early diagnosis is a key influencing factor for the prognosis of BA. Indocyanine green （ICG） is a near-infrared photosensitive dye that is efficiently and selectively absorbed by hepatocytes after intravenous injection， and it enters the intestine via bile and is excreted with the feces in the free form， with a favorable safety profile. In addition， ICG can emit fluorescence under near-infrared light， which can be captured by camera instruments and converted into visual images， and ICG fluorescence imaging technology can reflect the intraoperative situation in real time and significantly improve the success rate of the surgical procedure. This article reviews the advances in the application of ICG in early preoperative diagnosis， intraoperative imaging， and postoperative liver function assessment in recent years.

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

BACKGROUND: Fibrosis of the connective tissue in the vaginal wall predominates in pelvic organ prolapse (POP), which is characterized by excessive fibroblast-to-myofibroblast differentiation and abnormal deposition of the extracellular matrix (ECM). Our study aimed to investigate the effect of ECM stiffness on vaginal fibroblasts and to explore the role of methyltransferase 3 (METTL3) in the development of POP. METHODS: Polyacrylamide hydrogels were applied to create an ECM microenvironment with variable stiffness to evaluate the effects of ECM stiffness on the proliferation, differentiation, and expression of ECM components in vaginal fibroblasts. METTL3 small interfering RNA and an overexpression vector were transfected into vaginal fibroblasts to evaluate the effects of METTL3 silencing and overexpression on matrix stiffness-induced vaginal fibroblast-to-myofibroblast differentiation and abnormal modulation of the ECM. Both procedures were detected by 5-ethynyl-2'-deoxyuridine (EdU) staining, Western blotting (WB), quantitative real-time polymerase chain reaction (RT-qPCR), and immunofluorescence (IF). RESULTS: Vaginal fibroblasts from POP patients exhibited increased proliferation ability, increased expression of α-smooth muscle actin (α-SMA), decreased expression of collagen I/III, and significantly decreased expression of tissue inhibitors of matrix metalloproteinases (TIMPs) in the stiff matrix ( P <0.05). Compared with those from non-POP patients, vaginal wall tissues from POP patients demonstrated a significant increase in METTL3 content ( P <0.05). However, silencing METTL3 expression in vaginal fibroblasts with high ECM stiffness resulted in decreased proliferation ability, decreased α-SMA expression, an increased ratio of collagen I/III, and increased TIMP1 and TIMP2 expression. Conversely, METTL3 overexpression significantly promoted the process of increased proliferation ability, increased α-SMA expression, decreased ratio of collagen I/III and decreased TIMP1 and TIMP2 expression in the soft matrix ( P <0.05). CONCLUSIONS Elevated ECM stiffness can promote excessive proliferation, differentiation, and abnormal ECM modulation, and the expression of METTL3 plays an important role in alleviating or aggravating matrix stiffness-induced vaginal fibroblast-to-myofibroblast differentiation and abnormal ECM modulation.
Humans ; Female ; Extracellular Matrix/metabolism* ; Cell Differentiation/genetics* ; Methyltransferases/metabolism* ; Pelvic Organ Prolapse/pathology* ; Fibroblasts/metabolism* ; Myofibroblasts/metabolism* ; Vagina/metabolism* ; Cell Proliferation/physiology* ; Cells, Cultured ; Middle Aged

In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Acoustic Stimulation ; Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology* ; Adult